The use of biometric data for the identification of individuals is increasingly becoming the preferred choice in many environments due to the relative difficulty in fraudulently replicating the data. For example, due to increasing fraud involving payment cards such as credit cards, it has been proposed to use biometric data, such as for example fingerprints, to identify customers in shops or supermarkets to allow a payment transaction to be initiated. As a further example, biometric data is increasing used for identifying individuals authorized to enter restricted areas, such as gyms, border controls or vehicles. Furthermore, criminal databases have long been used for identifying individuals based on biometric data, such as a fingerprint or facial image taken at a crime scene.
To identify individuals, a biometric sample is obtained and compared to the records of a database, until a match is found. In the majority of applications, speed is of the essence. For example, if a user is at the checkout of a supermarket, or at a border control, an identification delay of more than several seconds may be considered unacceptable. A further requirement is that there are very few errors, i.e. very few false positive and false negative results. Indeed, if a customer at the checkout of a supermarket can not be identified, or is wrongly identified, this could lead to the customer being unable to make the payment, or to the wrong person being billed.
However, there is a technical problem in increasing the speed of identification and/or in reducing the error rate in current biometric identification systems.